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Objective:To explore the effect of triglyceride glucose(TyG) index, single nucleotide polymorphism of Toll-like receptor 4(TLR4) and NOD-like receptor thermal protein domain associated protein 3(NLRP3) genes, and its interaction on the risk of gout.Methods:A total of 315 male patients with gout and 499 men for health checkup at the same period were selected. General data were collected through questionnaires, and peripheral venous blood was collected for biochemical test. Three single nucleotide polymorphisms(SNPs) of NLRP3 and TLR4 were detected with multiplex ligase assay reaction, and logistic regression analysis was applied to compare the correlation between NLRP3 and TLR4 alleles and gout risk. The interaction of SNP and TyG index with gout was analyzed by generalized multi-factor dimensionality reduction(GMDR) model and logistic regression.Results:After adjusting for smoking, drinking, and other factors, the risk of gout increased by 61.1% for each standard deviation increase in TyG index. CC genotypes of rs10754558, rs10759932, and rs7525979 were high risk genotypes of gout in Han ethnicity. GMDR results showed significant differences in the interaction models of rs10754558-TyG index, rs7525979-TyG index, and rs10759932-TyG index between control group and gout group( P<0.05), suggesting an interaction between the three genotypes of SNPs selected and TyG index. Stratified analysis of the three selected SNPs and TyG index showed that after adjusting for age, smoking, and other factors, the high TyG index patients carrying C/C or C/G genotype at rs10754558 displayed an increased risk of gout compared with those carrying GG genotype and low TyG index( OR=2.127, P<0.05). Conclusion:The CC genotypes of rs10754558, rs10759932, and rs7525979 are high risk genotypes for gout in Han ethnicity. The interaction between rs10754558 and TyG index may increase the risk of gout development.
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Objective:To investigate the risk factors of gout and establish a columnar graph model to predict the risk of gout development.Methods:A total of 1 032 Han Chinese men attending the Affiliated Hospital of Traditional Chinese Medicine of Xinjiang Medical University, People′s Hospital of Xinjiang Uygur Autonomous Region, and the First Affiliated Hospital of Xinjiang Medical University from 2018 to 2020 were selected as study subjects and divided into training set(722 cases)and validation set(310 cases)by simple random sampling method in the ratio of 7∶3. General information and biochemical indices of the subjects were collected. The collected information was used to assess the risk of gout prevalence. LASSO regression analysis of R Studio software was used to screen the best predictors, and was introduced to construct a column line graph model for predicting gout risk using receiver operating characteristic(ROC)curves, and the Hosmer-Lemeshow test was used to assess the discrimination and calibration of the column line graph model. Finally, decision curve analysis(DCA)was performed using the rmda program package to assess the clinical utility of the model in validation data.Results:Age, uric acid, body mass index, total cholesterol, and waist-to-hip ratio were risk factors for gout( P<0.05). The column line graph prediction model based on the above five independent risk factors had good discrimination(AUC value: 0.923 for training set validation and 0.922 for validation set validation)and accuracy(Hosmer-Lemeshow test: P>0.05 for validation set validation); decision curve analysis showed that the prediction model curve had clinical practical value. Conclusion:The nomogram model established by combining age, uric acid, body mass index, total cholesterol, and waist-to-hip ratio indicators can predict the risk of gout more accurately.
ABSTRACT
@#Abstract: Objective To explore the relationship between body mass index (BMI), waist-hip ratio (WHR) and the risk of gout in Urumqi. Methods A total of 516 male patients with gout in a third-class hospital in Urumqi from 2015 to 2019 were randomly selected as the gout group and 516 male healthy subjects in the same hospital as the control group. The relevant blood biochemical indexes were examined and analyzed. Blood pressure, waist circumference and hip circumference were measured. Body mass index and waist-to-hip ratio were calculated. Logistic regression model was used to analyze the relationship between overweight / obesity, waist-to-hip ratio and the risk of gout. The test level is α = 0.05. Results Uric acid, glucose, urea nitrogen, creatinine, triglyceride, low-density lipoprotein, systolic blood pressure, weight and waist circumference in gout group were higher than those in control group, and the differences were statistically significant (P<0.05); There were no significant differences in age, height and diastolic blood pressure between the two groups (P<0.05). There was a positive correlation between BMI and WHR and the occurrence of gout (r=0.272, 0.345, P<0.05). There were significant differences in BMI, WHR and waist circumference between the gout group and the control group（χ2= 55.338, 54.928, 54.153, P<0.05）. After adjusting for age, aerobic exercise and other confounding factors, the results of multi-factor unconditional Logistic regression analysis showed that the odds ratio (OR) of gout in patients with BMI of 24.00-27.99 kg/m2 and ≥28.00 kg/m2 was 2.005 (1.337-3.006) and 2.677 (1.668-4.296) times higher than that of patients with normal BMI, respectively. The OR value of gout in patients with WHR≥0.90 was 1.668 times higher than that in patients with normal WHR, and the difference was statistically significant. The results of subgroup analysis according to age are generally similar. Conclusions The BMI and WHR of man with gout in Urumqi are higher than those of normal people, and BMI, waist circumference and WHR are all associated with the incidence of gout. The risk of gout increases with the increase of BMI and WHR.